Overview

Dataset statistics

Number of variables39
Number of observations119143
Missing cells204813
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.4 MiB
Average record size in memory320.0 B

Variable types

Text11
Categorical5
DateTime8
Numeric15

Alerts

price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
payment_value is highly overall correlated with priceHigh correlation
product_weight_g is highly overall correlated with price and 3 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
seller_zip_code_prefix is highly overall correlated with seller_stateHigh correlation
customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
seller_state is highly overall correlated with seller_zip_code_prefixHigh correlation
order_status is highly imbalanced (91.6%)Imbalance
payment_type is highly imbalanced (52.6%)Imbalance
seller_state is highly imbalanced (63.3%)Imbalance
order_delivered_carrier_date has 2086 (1.8%) missing valuesMissing
order_delivered_customer_date has 3421 (2.9%) missing valuesMissing
review_comment_title has 105154 (88.3%) missing valuesMissing
review_comment_message has 68898 (57.8%) missing valuesMissing
product_category_name has 2542 (2.1%) missing valuesMissing
product_name_lenght has 2542 (2.1%) missing valuesMissing
product_description_lenght has 2542 (2.1%) missing valuesMissing
product_photos_qty has 2542 (2.1%) missing valuesMissing

Reproduction

Analysis started2023-07-25 00:21:08.636415
Analysis finished2023-07-25 00:21:54.117148
Duration45.48 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Distinct99441
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:54.352760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3812576
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86494 ?
Unique (%)72.6%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd rowe481f51cbdc54678b7cc49136f2d6af7
3rd rowe481f51cbdc54678b7cc49136f2d6af7
4th row128e10d95713541c87cd1a2e48201934
5th row0e7e841ddf8f8f2de2bad69267ecfbcf
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c 63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a5547 38
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e39231352 29
 
< 0.1%
ccf804e764ed5650cd8759557269dc13 26
 
< 0.1%
c6492b842ac190db807c15aff21a7dd6 24
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec5 24
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc 24
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc 24
 
< 0.1%
68986e4324f6a21481df4e6e89abcf01 24
 
< 0.1%
285c2e15bebd4ac83635ccc563dc71f4 22
 
< 0.1%
Other values (99431) 118845
99.7%
2023-07-24T21:21:54.691775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 239371
 
6.3%
b 239344
 
6.3%
6 239250
 
6.3%
e 238956
 
6.3%
3 238724
 
6.3%
c 238563
 
6.3%
8 238518
 
6.3%
7 238501
 
6.3%
1 238422
 
6.3%
a 238162
 
6.2%
Other values (6) 1424765
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2382278
62.5%
Lowercase Letter 1430298
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 239371
10.0%
6 239250
10.0%
3 238724
10.0%
8 238518
10.0%
7 238501
10.0%
1 238422
10.0%
2 237912
10.0%
9 237674
10.0%
0 237062
10.0%
5 236844
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 239344
16.7%
e 238956
16.7%
c 238563
16.7%
a 238162
16.7%
f 237877
16.6%
d 237396
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2382278
62.5%
Latin 1430298
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 239371
10.0%
6 239250
10.0%
3 238724
10.0%
8 238518
10.0%
7 238501
10.0%
1 238422
10.0%
2 237912
10.0%
9 237674
10.0%
0 237062
10.0%
5 236844
9.9%
Latin
ValueCountFrequency (%)
b 239344
16.7%
e 238956
16.7%
c 238563
16.7%
a 238162
16.7%
f 237877
16.6%
d 237396
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3812576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 239371
 
6.3%
b 239344
 
6.3%
6 239250
 
6.3%
e 238956
 
6.3%
3 238724
 
6.3%
c 238563
 
6.3%
8 238518
 
6.3%
7 238501
 
6.3%
1 238422
 
6.3%
a 238162
 
6.2%
Other values (6) 1424765
37.4%
Distinct99441
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:54.940835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3812576
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86494 ?
Unique (%)72.6%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row9ef432eb6251297304e76186b10a928d
3rd row9ef432eb6251297304e76186b10a928d
4th rowa20e8105f23924cd00833fd87daa0831
5th row26c7ac168e1433912a51b924fbd34d34
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a83 63
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb 38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f34 29
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e2 26
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd2925 24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f4 24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf 24
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d329 24
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb 24
 
< 0.1%
b246eeed30b362c09d867b9e598bee51 22
 
< 0.1%
Other values (99431) 118845
99.7%
2023-07-24T21:21:55.272147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 239014
 
6.3%
2 238989
 
6.3%
5 238748
 
6.3%
c 238647
 
6.3%
6 238555
 
6.3%
1 238489
 
6.3%
8 238361
 
6.3%
d 238357
 
6.3%
7 238270
 
6.2%
a 238267
 
6.2%
Other values (6) 1426879
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2381876
62.5%
Lowercase Letter 1430700
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 238989
10.0%
5 238748
10.0%
6 238555
10.0%
1 238489
10.0%
8 238361
10.0%
7 238270
10.0%
3 238161
10.0%
9 238003
10.0%
4 237276
10.0%
0 237024
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 239014
16.7%
c 238647
16.7%
d 238357
16.7%
a 238267
16.7%
e 238226
16.7%
b 238189
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2381876
62.5%
Latin 1430700
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 238989
10.0%
5 238748
10.0%
6 238555
10.0%
1 238489
10.0%
8 238361
10.0%
7 238270
10.0%
3 238161
10.0%
9 238003
10.0%
4 237276
10.0%
0 237024
10.0%
Latin
ValueCountFrequency (%)
f 239014
16.7%
c 238647
16.7%
d 238357
16.7%
a 238267
16.7%
e 238226
16.7%
b 238189
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3812576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 239014
 
6.3%
2 238989
 
6.3%
5 238748
 
6.3%
c 238647
 
6.3%
6 238555
 
6.3%
1 238489
 
6.3%
8 238361
 
6.3%
d 238357
 
6.3%
7 238270
 
6.2%
a 238267
 
6.2%
Other values (6) 1426879
37.4%

order_status
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
delivered
115723 
shipped
 
1256
canceled
 
750
unavailable
 
652
invoiced
 
378
Other values (3)
 
384

Length

Max length11
Median length9
Mean length8.9834401
Min length7

Characters and Unicode

Total characters1070314
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 115723
97.1%
shipped 1256
 
1.1%
canceled 750
 
0.6%
unavailable 652
 
0.5%
invoiced 378
 
0.3%
processing 376
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Length

2023-07-24T21:21:55.397704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:21:55.518735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
delivered 115723
97.1%
shipped 1256
 
1.1%
canceled 750
 
0.6%
unavailable 652
 
0.5%
invoiced 378
 
0.3%
processing 376
 
0.3%
created 5
 
< 0.1%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1070314
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1070314
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1070314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 351344
32.8%
d 233838
21.8%
i 118763
 
11.1%
l 117777
 
11.0%
v 116756
 
10.9%
r 116107
 
10.8%
p 2894
 
0.3%
a 2714
 
0.3%
c 2259
 
0.2%
n 2156
 
0.2%
Other values (7) 5706
 
0.5%
Distinct98875
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-09-04 21:15:19
Maximum2018-10-17 17:30:18
2023-07-24T21:21:55.631760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:55.745786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct90733
Distinct (%)76.3%
Missing177
Missing (%)0.1%
Memory size1.8 MiB
Minimum2016-09-15 12:16:38
Maximum2018-09-03 17:40:06
2023-07-24T21:21:55.872814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:55.977838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct81018
Distinct (%)69.2%
Missing2086
Missing (%)1.8%
Memory size1.8 MiB
Minimum2016-10-08 10:34:01
Maximum2018-09-11 19:48:28
2023-07-24T21:21:56.089864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:56.195888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct95664
Distinct (%)82.7%
Missing3421
Missing (%)2.9%
Memory size1.8 MiB
Minimum2016-10-11 13:46:32
Maximum2018-10-17 13:22:46
2023-07-24T21:21:56.308912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:56.418938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct459
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2016-09-30 00:00:00
Maximum2018-11-12 00:00:00
2023-07-24T21:21:56.535964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:56.647989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.196543
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:56.754013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6994889
Coefficient of variation (CV)0.58459154
Kurtosis103.35482
Mean1.196543
Median Absolute Deviation (MAD)0
Skewness7.5517266
Sum141563
Variance0.48928472
MonotonicityNot monotonic
2023-07-24T21:21:56.846034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 103645
87.0%
2 10317
 
8.7%
3 2396
 
2.0%
4 995
 
0.8%
5 472
 
0.4%
6 265
 
0.2%
7 61
 
0.1%
8 37
 
< 0.1%
9 29
 
< 0.1%
10 26
 
< 0.1%
Other values (11) 67
 
0.1%
(Missing) 833
 
0.7%
ValueCountFrequency (%)
1 103645
87.0%
2 10317
 
8.7%
3 2396
 
2.0%
4 995
 
0.8%
5 472
 
0.4%
6 265
 
0.2%
7 61
 
0.1%
8 37
 
< 0.1%
9 29
 
< 0.1%
10 26
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
< 0.1%
19 3
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 3
 
< 0.1%
15 5
 
< 0.1%
14 7
< 0.1%
13 8
< 0.1%
12 13
< 0.1%
Distinct32951
Distinct (%)27.9%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
2023-07-24T21:21:57.034076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3785920
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17345 ?
Unique (%)14.7%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row87285b34884572647811a353c7ac498a
3rd row87285b34884572647811a353c7ac498a
4th row87285b34884572647811a353c7ac498a
5th row87285b34884572647811a353c7ac498a
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 536
 
0.5%
99a4788cb24856965c36a24e339b6058 528
 
0.4%
422879e10f46682990de24d770e7f83d 508
 
0.4%
389d119b48cf3043d311335e499d9c6b 406
 
0.3%
368c6c730842d78016ad823897a372db 398
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 391
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 357
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 327
 
0.3%
154e7e31ebfa092203795c972e5804a6 295
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7 278
 
0.2%
Other values (32941) 114286
96.6%
2023-07-24T21:21:57.322651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 243128
 
6.4%
9 241092
 
6.4%
e 238897
 
6.3%
8 238246
 
6.3%
7 238157
 
6.3%
4 237487
 
6.3%
a 237289
 
6.3%
c 236394
 
6.2%
0 236277
 
6.2%
2 236110
 
6.2%
Other values (6) 1402843
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2375835
62.8%
Lowercase Letter 1410085
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 243128
10.2%
9 241092
10.1%
8 238246
10.0%
7 238157
10.0%
4 237487
10.0%
0 236277
9.9%
2 236110
9.9%
6 235751
9.9%
5 235615
9.9%
1 233972
9.8%
Lowercase Letter
ValueCountFrequency (%)
e 238897
16.9%
a 237289
16.8%
c 236394
16.8%
b 235053
16.7%
d 232751
16.5%
f 229701
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2375835
62.8%
Latin 1410085
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 243128
10.2%
9 241092
10.1%
8 238246
10.0%
7 238157
10.0%
4 237487
10.0%
0 236277
9.9%
2 236110
9.9%
6 235751
9.9%
5 235615
9.9%
1 233972
9.8%
Latin
ValueCountFrequency (%)
e 238897
16.9%
a 237289
16.8%
c 236394
16.8%
b 235053
16.7%
d 232751
16.5%
f 229701
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3785920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 243128
 
6.4%
9 241092
 
6.4%
e 238897
 
6.3%
8 238246
 
6.3%
7 238157
 
6.3%
4 237487
 
6.3%
a 237289
 
6.3%
c 236394
 
6.2%
0 236277
 
6.2%
2 236110
 
6.2%
Other values (6) 1402843
37.1%
Distinct3095
Distinct (%)2.6%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
2023-07-24T21:21:57.498679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3785920
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique487 ?
Unique (%)0.4%

Sample

1st row3504c0cb71d7fa48d967e0e4c94d59d9
2nd row3504c0cb71d7fa48d967e0e4c94d59d9
3rd row3504c0cb71d7fa48d967e0e4c94d59d9
4th row3504c0cb71d7fa48d967e0e4c94d59d9
5th row3504c0cb71d7fa48d967e0e4c94d59d9
ValueCountFrequency (%)
4a3ca9315b744ce9f8e9374361493884 2155
 
1.8%
6560211a19b47992c3666cc44a7e94c0 2130
 
1.8%
1f50f920176fa81dab994f9023523100 2017
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1893
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1662
 
1.4%
955fee9216a65b617aa5c0531780ce60 1530
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1477
 
1.2%
7c67e1448b00f6e969d365cea6b010ab 1463
 
1.2%
7a67c85e85bb2ce8582c35f2203ad736 1245
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc 1240
 
1.0%
Other values (3085) 101498
85.8%
2023-07-24T21:21:58.012119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 256833
 
6.8%
c 250006
 
6.6%
4 248481
 
6.6%
6 243514
 
6.4%
0 242843
 
6.4%
a 241350
 
6.4%
b 240801
 
6.4%
3 240746
 
6.4%
9 235027
 
6.2%
2 233698
 
6.2%
Other values (6) 1352621
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2394603
63.3%
Lowercase Letter 1391317
36.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 256833
10.7%
4 248481
10.4%
6 243514
10.2%
0 242843
10.1%
3 240746
10.1%
9 235027
9.8%
2 233698
9.8%
8 231747
9.7%
5 231055
9.6%
7 230659
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 250006
18.0%
a 241350
17.3%
b 240801
17.3%
e 222598
16.0%
f 219169
15.8%
d 217393
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2394603
63.3%
Latin 1391317
36.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 256833
10.7%
4 248481
10.4%
6 243514
10.2%
0 242843
10.1%
3 240746
10.1%
9 235027
9.8%
2 233698
9.8%
8 231747
9.7%
5 231055
9.6%
7 230659
9.6%
Latin
ValueCountFrequency (%)
c 250006
18.0%
a 241350
17.3%
b 240801
17.3%
e 222598
16.0%
f 219169
15.8%
d 217393
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3785920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 256833
 
6.8%
c 250006
 
6.6%
4 248481
 
6.6%
6 243514
 
6.4%
0 242843
 
6.4%
a 241350
 
6.4%
b 240801
 
6.4%
3 240746
 
6.4%
9 235027
 
6.2%
2 233698
 
6.2%
Other values (6) 1352621
35.7%
Distinct93318
Distinct (%)78.9%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
Minimum2016-09-19 00:15:34
Maximum2020-04-09 22:35:08
2023-07-24T21:21:58.140655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:58.251080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

Distinct5968
Distinct (%)5.0%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean120.6466
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:58.366106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation184.10969
Coefficient of variation (CV)1.5260247
Kurtosis119.15494
Mean120.6466
Median Absolute Deviation (MAD)42
Skewness7.8925735
Sum14273700
Variance33896.378
MonotonicityNot monotonic
2023-07-24T21:21:58.475131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2619
 
2.2%
69.9 2113
 
1.8%
49.9 2051
 
1.7%
89.9 1644
 
1.4%
99.9 1526
 
1.3%
39.9 1403
 
1.2%
29.9 1387
 
1.2%
19.9 1284
 
1.1%
79.9 1282
 
1.1%
29.99 1228
 
1.0%
Other values (5958) 101773
85.4%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%

freight_value
Real number (ℝ)

Distinct6999
Distinct (%)5.9%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean20.032387
Minimum0
Maximum409.68
Zeros390
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:58.594746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.28
Q321.18
95-th percentile45.3
Maximum409.68
Range409.68
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation15.83685
Coefficient of variation (CV)0.79056234
Kurtosis57.635327
Mean20.032387
Median Absolute Deviation (MAD)3.63
Skewness5.5433839
Sum2370031.6
Variance250.80583
MonotonicityNot monotonic
2023-07-24T21:21:58.702086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3861
 
3.2%
7.78 2355
 
2.0%
11.85 1999
 
1.7%
14.1 1992
 
1.7%
18.23 1632
 
1.4%
7.39 1573
 
1.3%
16.11 1211
 
1.0%
15.23 1064
 
0.9%
8.72 970
 
0.8%
16.79 930
 
0.8%
Other values (6989) 100723
84.5%
ValueCountFrequency (%)
0 390
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 9
 
< 0.1%
0.06 13
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%

payment_sequential
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.0947373
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:58.803705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73014099
Coefficient of variation (CV)0.66695545
Kurtosis342.28301
Mean1.0947373
Median Absolute Deviation (MAD)0
Skewness15.775506
Sum130427
Variance0.53310587
MonotonicityNot monotonic
2023-07-24T21:21:58.895749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 113999
95.7%
2 3415
 
2.9%
3 658
 
0.6%
4 322
 
0.3%
5 194
 
0.2%
6 136
 
0.1%
7 94
 
0.1%
8 63
 
0.1%
9 51
 
< 0.1%
10 42
 
< 0.1%
Other values (19) 166
 
0.1%
ValueCountFrequency (%)
1 113999
95.7%
2 3415
 
2.9%
3 658
 
0.6%
4 322
 
0.3%
5 194
 
0.2%
6 136
 
0.1%
7 94
 
0.1%
8 63
 
0.1%
9 51
 
< 0.1%
10 42
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 2
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
22 3
< 0.1%
21 6
< 0.1%
20 6
< 0.1%

payment_type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size1.8 MiB
credit_card
87776 
boleto
23190 
voucher
 
6465
debit_card
 
1706
not_defined
 
3

Length

Max length11
Median length11
Mean length9.7954004
Min length6

Characters and Unicode

Total characters1167024
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowvoucher
3rd rowvoucher
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 87776
73.7%
boleto 23190
 
19.5%
voucher 6465
 
5.4%
debit_card 1706
 
1.4%
not_defined 3
 
< 0.1%
(Missing) 3
 
< 0.1%

Length

2023-07-24T21:21:58.993670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:21:59.091057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 87776
73.7%
boleto 23190
 
19.5%
voucher 6465
 
5.4%
debit_card 1706
 
1.4%
not_defined 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1077539
92.3%
Connector Punctuation 89485
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 183723
17.1%
r 183723
17.1%
d 178970
16.6%
e 119143
11.1%
t 112675
10.5%
i 89485
8.3%
a 89482
8.3%
o 52848
 
4.9%
b 24896
 
2.3%
l 23190
 
2.2%
Other values (5) 19404
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_ 89485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1077539
92.3%
Common 89485
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 183723
17.1%
r 183723
17.1%
d 178970
16.6%
e 119143
11.1%
t 112675
10.5%
i 89485
8.3%
a 89482
8.3%
o 52848
 
4.9%
b 24896
 
2.3%
l 23190
 
2.2%
Other values (5) 19404
 
1.8%
Common
ValueCountFrequency (%)
_ 89485
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 183723
15.7%
r 183723
15.7%
d 178970
15.3%
e 119143
10.2%
t 112675
9.7%
i 89485
7.7%
_ 89485
7.7%
a 89482
7.7%
o 52848
 
4.5%
b 24896
 
2.1%
Other values (6) 42594
 
3.6%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.9412456
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:59.179840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7778477
Coefficient of variation (CV)0.94444604
Kurtosis2.5065453
Mean2.9412456
Median Absolute Deviation (MAD)1
Skewness1.6198199
Sum350420
Variance7.7164381
MonotonicityNot monotonic
2023-07-24T21:21:59.277862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 59446
49.9%
2 13838
 
11.6%
3 11889
 
10.0%
4 8072
 
6.8%
10 6976
 
5.9%
5 6097
 
5.1%
8 5120
 
4.3%
6 4674
 
3.9%
7 1848
 
1.6%
9 739
 
0.6%
Other values (14) 441
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 59446
49.9%
2 13838
 
11.6%
3 11889
 
10.0%
4 8072
 
6.8%
5 6097
 
5.1%
6 4674
 
3.9%
7 1848
 
1.6%
8 5120
 
4.3%
9 739
 
0.6%
ValueCountFrequency (%)
24 34
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 6
 
< 0.1%
20 21
 
< 0.1%
18 38
< 0.1%
17 8
 
< 0.1%
16 7
 
< 0.1%
15 93
0.1%
14 16
 
< 0.1%

payment_value
Real number (ℝ)

Distinct29077
Distinct (%)24.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean172.73514
Minimum0
Maximum13664.08
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:21:59.379885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.1
Q160.85
median108.16
Q3189.24
95-th percentile515.93
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.39

Descriptive statistics

Standard deviation267.77608
Coefficient of variation (CV)1.550212
Kurtosis500.3632
Mean172.73514
Median Absolute Deviation (MAD)56.64
Skewness13.965989
Sum20579664
Variance71704.027
MonotonicityNot monotonic
2023-07-24T21:21:59.486909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 351
 
0.3%
100 300
 
0.3%
20 286
 
0.2%
77.57 250
 
0.2%
35 166
 
0.1%
73.34 160
 
0.1%
30 139
 
0.1%
116.94 133
 
0.1%
56.78 123
 
0.1%
65 120
 
0.1%
Other values (29067) 117112
98.3%
ValueCountFrequency (%)
0 9
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 2
 
< 0.1%
0.13 2
 
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6922.21 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
4950.34 1
 
< 0.1%
4809.44 2
 
< 0.1%
4764.34 1
 
< 0.1%
4681.78 1
 
< 0.1%
Distinct98410
Distinct (%)83.3%
Missing997
Missing (%)0.8%
Memory size1.8 MiB
2023-07-24T21:21:59.722962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3780672
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85411 ?
Unique (%)72.3%

Sample

1st rowa54f0611adc9ed256b57ede6b6eb5114
2nd rowa54f0611adc9ed256b57ede6b6eb5114
3rd rowa54f0611adc9ed256b57ede6b6eb5114
4th rowb46f1e34512b0f4c74a72398b03ca788
5th rowdc90f19c2806f1abba9e72ad3c350073
ValueCountFrequency (%)
eef5dbca8d37dfce6db7d7b16dd0525e 63
 
0.1%
7145a6f0d38ec713897856cbdcfcdb7f 38
 
< 0.1%
f28281373ab8815bafafe371218f02ce 29
 
< 0.1%
8823bba1e3301fee652eb06de8ef9435 26
 
< 0.1%
b0c2f8c122ebef9f77753f7d167cf634 24
 
< 0.1%
b79b22bb50f78f1afe361661011fd892 24
 
< 0.1%
cc074f1c33940c4f0dd904705f98e39e 24
 
< 0.1%
b5292206f96cd5d97359940203a0b510 24
 
< 0.1%
7e568736c98c553aea896a5dca746d5a 22
 
< 0.1%
8fb71ed887db39231871ef3d1ba781cf 21
 
< 0.1%
Other values (98400) 117851
99.8%
2023-07-24T21:22:00.051036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 237115
 
6.3%
6 237048
 
6.3%
5 236734
 
6.3%
b 236598
 
6.3%
d 236586
 
6.3%
8 236562
 
6.3%
f 236490
 
6.3%
1 236390
 
6.3%
0 236298
 
6.3%
7 236091
 
6.2%
Other values (6) 1414760
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2361788
62.5%
Lowercase Letter 1418884
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 237048
10.0%
5 236734
10.0%
8 236562
10.0%
1 236390
10.0%
0 236298
10.0%
7 236091
10.0%
2 235983
10.0%
9 235791
10.0%
3 235541
10.0%
4 235350
10.0%
Lowercase Letter
ValueCountFrequency (%)
a 237115
16.7%
b 236598
16.7%
d 236586
16.7%
f 236490
16.7%
e 236068
16.6%
c 236027
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2361788
62.5%
Latin 1418884
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 237048
10.0%
5 236734
10.0%
8 236562
10.0%
1 236390
10.0%
0 236298
10.0%
7 236091
10.0%
2 235983
10.0%
9 235791
10.0%
3 235541
10.0%
4 235350
10.0%
Latin
ValueCountFrequency (%)
a 237115
16.7%
b 236598
16.7%
d 236586
16.7%
f 236490
16.7%
e 236068
16.6%
c 236027
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3780672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 237115
 
6.3%
6 237048
 
6.3%
5 236734
 
6.3%
b 236598
 
6.3%
d 236586
 
6.3%
8 236562
 
6.3%
f 236490
 
6.3%
1 236390
 
6.3%
0 236298
 
6.3%
7 236091
 
6.2%
Other values (6) 1414760
37.4%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing997
Missing (%)0.8%
Memory size1.8 MiB
5.0
66343 
4.0
22319 
1.0
15428 
3.0
9894 
2.0
 
4162

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354438
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 66343
55.7%
4.0 22319
 
18.7%
1.0 15428
 
12.9%
3.0 9894
 
8.3%
2.0 4162
 
3.5%
(Missing) 997
 
0.8%

Length

2023-07-24T21:22:00.167062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-24T21:22:00.266085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 66343
56.2%
4.0 22319
 
18.9%
1.0 15428
 
13.1%
3.0 9894
 
8.4%
2.0 4162
 
3.5%

Most occurring characters

ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236292
66.7%
Other Punctuation 118146
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118146
50.0%
5 66343
28.1%
4 22319
 
9.4%
1 15428
 
6.5%
3 9894
 
4.2%
2 4162
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 118146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 354438
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354438
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118146
33.3%
0 118146
33.3%
5 66343
18.7%
4 22319
 
6.3%
1 15428
 
4.4%
3 9894
 
2.8%
2 4162
 
1.2%
Distinct4527
Distinct (%)32.4%
Missing105154
Missing (%)88.3%
Memory size1.8 MiB
2023-07-24T21:22:00.479133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length20
Mean length12.213525
Min length1

Characters and Unicode

Total characters170855
Distinct characters125
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3095 ?
Unique (%)22.1%

Sample

1st rowMuito boa a loja
2nd rowsuper recomendo
3rd rowMuito bom
4th rowsuper recomendo
5th rowsuper recomendo
ValueCountFrequency (%)
recomendo 2478
 
9.3%
produto 1570
 
5.9%
bom 1521
 
5.7%
muito 1040
 
3.9%
super 1039
 
3.9%
não 937
 
3.5%
ótimo 809
 
3.0%
excelente 769
 
2.9%
entrega 716
 
2.7%
recebi 445
 
1.7%
Other values (2100) 15445
57.7%
2023-07-24T21:22:00.818681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 21273
 
12.5%
e 18299
 
10.7%
15200
 
8.9%
r 9902
 
5.8%
t 9433
 
5.5%
a 9158
 
5.4%
m 8444
 
4.9%
d 8214
 
4.8%
i 8084
 
4.7%
n 7660
 
4.5%
Other values (115) 55188
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 132750
77.7%
Uppercase Letter 18878
 
11.0%
Space Separator 15200
 
8.9%
Other Punctuation 2683
 
1.6%
Decimal Number 1242
 
0.7%
Other Symbol 48
 
< 0.1%
Dash Punctuation 19
 
< 0.1%
Modifier Symbol 13
 
< 0.1%
Math Symbol 8
 
< 0.1%
Close Punctuation 7
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 21273
16.0%
e 18299
13.8%
r 9902
 
7.5%
t 9433
 
7.1%
a 9158
 
6.9%
m 8444
 
6.4%
d 8214
 
6.2%
i 8084
 
6.1%
n 7660
 
5.8%
c 5955
 
4.5%
Other values (31) 26328
19.8%
Uppercase Letter
ValueCountFrequency (%)
E 2533
13.4%
R 2048
10.8%
O 1675
 
8.9%
P 1617
 
8.6%
M 1448
 
7.7%
N 1205
 
6.4%
S 1046
 
5.5%
A 989
 
5.2%
Ó 933
 
4.9%
B 896
 
4.7%
Other values (26) 4488
23.8%
Other Punctuation
ValueCountFrequency (%)
! 1208
45.0%
. 787
29.3%
* 404
 
15.1%
, 152
 
5.7%
? 52
 
1.9%
/ 39
 
1.5%
% 22
 
0.8%
: 6
 
0.2%
; 4
 
0.1%
" 3
 
0.1%
Other values (4) 6
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 468
37.7%
1 413
33.3%
5 82
 
6.6%
2 77
 
6.2%
8 46
 
3.7%
3 43
 
3.5%
4 39
 
3.1%
9 29
 
2.3%
7 24
 
1.9%
6 21
 
1.7%
Other Symbol
ValueCountFrequency (%)
👍 16
33.3%
😍 9
18.8%
👏 7
14.6%
🌟 6
 
12.5%
💥 5
 
10.4%
🚚 1
 
2.1%
👎 1
 
2.1%
😀 1
 
2.1%
🔟 1
 
2.1%
🤗 1
 
2.1%
Modifier Symbol
ValueCountFrequency (%)
´ 6
46.2%
🏻 3
23.1%
🏽 2
 
15.4%
🏼 2
 
15.4%
Math Symbol
ValueCountFrequency (%)
+ 7
87.5%
= 1
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 6
85.7%
] 1
 
14.3%
Space Separator
ValueCountFrequency (%)
15200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 151629
88.7%
Common 19226
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 21273
14.0%
e 18299
12.1%
r 9902
 
6.5%
t 9433
 
6.2%
a 9158
 
6.0%
m 8444
 
5.6%
d 8214
 
5.4%
i 8084
 
5.3%
n 7660
 
5.1%
c 5955
 
3.9%
Other values (68) 45207
29.8%
Common
ValueCountFrequency (%)
15200
79.1%
! 1208
 
6.3%
. 787
 
4.1%
0 468
 
2.4%
1 413
 
2.1%
* 404
 
2.1%
, 152
 
0.8%
5 82
 
0.4%
2 77
 
0.4%
? 52
 
0.3%
Other values (37) 383
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167210
97.9%
None 3635
 
2.1%
Emoticons 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 21273
12.7%
e 18299
 
10.9%
15200
 
9.1%
r 9902
 
5.9%
t 9433
 
5.6%
a 9158
 
5.5%
m 8444
 
5.0%
d 8214
 
4.9%
i 8084
 
4.8%
n 7660
 
4.6%
Other values (75) 51543
30.8%
None
ValueCountFrequency (%)
ã 1125
30.9%
Ó 933
25.7%
á 360
 
9.9%
ç 347
 
9.5%
é 257
 
7.1%
ó 246
 
6.8%
à 71
 
2.0%
í 67
 
1.8%
ê 47
 
1.3%
É 30
 
0.8%
Other values (28) 152
 
4.2%
Emoticons
ValueCountFrequency (%)
😍 9
90.0%
😀 1
 
10.0%
Distinct36159
Distinct (%)72.0%
Missing68898
Missing (%)57.8%
Memory size1.8 MiB
2023-07-24T21:22:01.079037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length208
Median length159
Mean length70.656662
Min length1

Characters and Unicode

Total characters3550144
Distinct characters209
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29556 ?
Unique (%)58.8%

Sample

1st rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
2nd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
3rd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
4th rowDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.
5th rowSó achei ela pequena pra seis xícaras ,mais é um bom produto
ValueCountFrequency (%)
o 23016
 
3.8%
produto 21207
 
3.5%
e 20046
 
3.3%
a 15246
 
2.5%
de 14566
 
2.4%
não 13436
 
2.2%
do 13109
 
2.2%
que 10560
 
1.7%
prazo 9427
 
1.6%
muito 9086
 
1.5%
Other values (19737) 456676
75.3%
2023-07-24T21:22:01.459080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
562672
15.8%
o 350271
 
9.9%
e 340009
 
9.6%
a 282074
 
7.9%
r 200163
 
5.6%
i 163758
 
4.6%
t 161344
 
4.5%
d 150134
 
4.2%
n 138033
 
3.9%
s 134085
 
3.8%
Other values (199) 1067601
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2682795
75.6%
Space Separator 562672
 
15.8%
Uppercase Letter 168680
 
4.8%
Other Punctuation 96429
 
2.7%
Decimal Number 22010
 
0.6%
Control 14006
 
0.4%
Dash Punctuation 984
 
< 0.1%
Close Punctuation 750
 
< 0.1%
Open Punctuation 731
 
< 0.1%
Other Symbol 703
 
< 0.1%
Other values (5) 384
 
< 0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
👏 241
34.3%
👍 88
 
12.5%
😍 76
 
10.8%
° 27
 
3.8%
😉 23
 
3.3%
😘 20
 
2.8%
😡 19
 
2.7%
😆 19
 
2.7%
👎 13
 
1.8%
😁 13
 
1.8%
Other values (55) 164
23.3%
Lowercase Letter
ValueCountFrequency (%)
o 350271
13.1%
e 340009
12.7%
a 282074
10.5%
r 200163
 
7.5%
i 163758
 
6.1%
t 161344
 
6.0%
d 150134
 
5.6%
n 138033
 
5.1%
s 134085
 
5.0%
m 128515
 
4.8%
Other values (40) 634409
23.6%
Uppercase Letter
ValueCountFrequency (%)
E 19919
11.8%
O 18865
11.2%
A 17497
10.4%
P 12531
 
7.4%
R 12209
 
7.2%
C 9839
 
5.8%
N 9688
 
5.7%
M 9486
 
5.6%
S 8362
 
5.0%
T 7870
 
4.7%
Other values (31) 42414
25.1%
Other Punctuation
ValueCountFrequency (%)
. 50682
52.6%
, 28154
29.2%
! 12761
 
13.2%
/ 1881
 
2.0%
? 1630
 
1.7%
" 440
 
0.5%
: 307
 
0.3%
; 233
 
0.2%
% 194
 
0.2%
* 78
 
0.1%
Other values (5) 69
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 5074
23.1%
1 5056
23.0%
2 4230
19.2%
3 1967
 
8.9%
4 1367
 
6.2%
5 1246
 
5.7%
8 946
 
4.3%
6 905
 
4.1%
7 753
 
3.4%
9 466
 
2.1%
Math Symbol
ValueCountFrequency (%)
+ 88
59.9%
= 29
 
19.7%
| 12
 
8.2%
< 10
 
6.8%
~ 3
 
2.0%
× 2
 
1.4%
> 2
 
1.4%
÷ 1
 
0.7%
Modifier Symbol
ValueCountFrequency (%)
🏻 36
34.6%
´ 26
25.0%
🏼 15
14.4%
🏽 13
 
12.5%
^ 8
 
7.7%
🏾 4
 
3.8%
` 2
 
1.9%
Control
ValueCountFrequency (%)
6993
49.9%
6993
49.9%
20
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 747
99.6%
] 3
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 725
99.2%
[ 6
 
0.8%
Other Letter
ValueCountFrequency (%)
º 26
57.8%
ª 19
42.2%
Space Separator
ValueCountFrequency (%)
562672
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 984
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 80
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2851520
80.3%
Common 698624
 
19.7%

Most frequent character per script

Common
ValueCountFrequency (%)
562672
80.5%
. 50682
 
7.3%
, 28154
 
4.0%
! 12761
 
1.8%
6993
 
1.0%
6993
 
1.0%
0 5074
 
0.7%
1 5056
 
0.7%
2 4230
 
0.6%
3 1967
 
0.3%
Other values (106) 14042
 
2.0%
Latin
ValueCountFrequency (%)
o 350271
12.3%
e 340009
11.9%
a 282074
 
9.9%
r 200163
 
7.0%
i 163758
 
5.7%
t 161344
 
5.7%
d 150134
 
5.3%
n 138033
 
4.8%
s 134085
 
4.7%
m 128515
 
4.5%
Other values (83) 803134
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3485523
98.2%
None 64369
 
1.8%
Emoticons 252
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
562672
16.1%
o 350271
 
10.0%
e 340009
 
9.8%
a 282074
 
8.1%
r 200163
 
5.7%
i 163758
 
4.7%
t 161344
 
4.6%
d 150134
 
4.3%
n 138033
 
4.0%
s 134085
 
3.8%
Other values (85) 1002980
28.8%
None
ValueCountFrequency (%)
ã 19200
29.8%
é 11589
18.0%
á 9179
14.3%
ç 7483
 
11.6%
ó 6322
 
9.8%
ê 1962
 
3.0%
í 1794
 
2.8%
Ó 1563
 
2.4%
õ 948
 
1.5%
ú 913
 
1.4%
Other values (73) 3416
 
5.3%
Emoticons
ValueCountFrequency (%)
😍 76
30.2%
😉 23
 
9.1%
😘 20
 
7.9%
😡 19
 
7.5%
😆 19
 
7.5%
😁 13
 
5.2%
😊 12
 
4.8%
😀 8
 
3.2%
😩 7
 
2.8%
😃 6
 
2.4%
Other values (21) 49
19.4%
Distinct636
Distinct (%)0.5%
Missing997
Missing (%)0.8%
Memory size1.8 MiB
Minimum2016-10-02 00:00:00
Maximum2018-08-31 00:00:00
2023-07-24T21:22:01.581108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:22:01.686131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct98248
Distinct (%)83.2%
Missing997
Missing (%)0.8%
Memory size1.8 MiB
Minimum2016-10-07 18:32:28
Maximum2018-10-29 12:27:35
2023-07-24T21:22:01.797683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:22:01.905785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct73
Distinct (%)0.1%
Missing2542
Missing (%)2.1%
Memory size1.8 MiB
2023-07-24T21:22:02.057065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length46
Median length32
Mean length14.876202
Min length3

Characters and Unicode

Total characters1734580
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowutilidades_domesticas
2nd rowutilidades_domesticas
3rd rowutilidades_domesticas
4th rowutilidades_domesticas
5th rowutilidades_domesticas
ValueCountFrequency (%)
cama_mesa_banho 11988
 
10.3%
beleza_saude 10032
 
8.6%
esporte_lazer 9004
 
7.7%
moveis_decoracao 8832
 
7.6%
informatica_acessorios 8150
 
7.0%
utilidades_domesticas 7380
 
6.3%
relogios_presentes 6213
 
5.3%
telefonia 4726
 
4.1%
ferramentas_jardim 4590
 
3.9%
automotivo 4400
 
3.8%
Other values (63) 41286
35.4%
2023-07-24T21:22:02.315123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 210541
12.1%
a 208834
12.0%
s 172854
10.0%
o 171612
9.9%
i 115171
 
6.6%
r 111455
 
6.4%
_ 110493
 
6.4%
t 83288
 
4.8%
c 82435
 
4.8%
m 78683
 
4.5%
Other values (18) 389214
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1623786
93.6%
Connector Punctuation 110493
 
6.4%
Decimal Number 301
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 210541
13.0%
a 208834
12.9%
s 172854
10.6%
o 171612
10.6%
i 115171
 
7.1%
r 111455
 
6.9%
t 83288
 
5.1%
c 82435
 
5.1%
m 78683
 
4.8%
n 59158
 
3.6%
Other values (16) 329755
20.3%
Connector Punctuation
ValueCountFrequency (%)
_ 110493
100.0%
Decimal Number
ValueCountFrequency (%)
2 301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1623786
93.6%
Common 110794
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 210541
13.0%
a 208834
12.9%
s 172854
10.6%
o 171612
10.6%
i 115171
 
7.1%
r 111455
 
6.9%
t 83288
 
5.1%
c 82435
 
5.1%
m 78683
 
4.8%
n 59158
 
3.6%
Other values (16) 329755
20.3%
Common
ValueCountFrequency (%)
_ 110493
99.7%
2 301
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1734580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 210541
12.1%
a 208834
12.0%
s 172854
10.0%
o 171612
9.9%
i 115171
 
6.6%
r 111455
 
6.4%
_ 110493
 
6.4%
t 83288
 
4.8%
c 82435
 
4.8%
m 78683
 
4.5%
Other values (18) 389214
22.4%

product_name_lenght
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing2542
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean48.767498
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:02.434947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.03354
Coefficient of variation (CV)0.20574236
Kurtosis0.14950788
Mean48.767498
Median Absolute Deviation (MAD)6
Skewness-0.90489402
Sum5686339
Variance100.67193
MonotonicityNot monotonic
2023-07-24T21:22:02.541973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8679
 
7.3%
60 8070
 
6.8%
56 6847
 
5.7%
58 6819
 
5.7%
57 6302
 
5.3%
55 5833
 
4.9%
54 5529
 
4.6%
53 4357
 
3.7%
52 4328
 
3.6%
49 3690
 
3.1%
Other values (56) 56147
47.1%
ValueCountFrequency (%)
5 9
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 15
 
< 0.1%
10 9
 
< 0.1%
11 11
 
< 0.1%
12 38
< 0.1%
13 26
< 0.1%
14 47
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 3
 
< 0.1%
66 1
 
< 0.1%
64 174
 
0.1%
63 1350
1.1%
62 167
 
0.1%
61 241
 
0.2%
Distinct2960
Distinct (%)2.5%
Missing2542
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean785.96782
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:02.658999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile160
Q1346
median600
Q3983
95-th percentile2123
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation652.58412
Coefficient of variation (CV)0.83029369
Kurtosis4.9299321
Mean785.96782
Median Absolute Deviation (MAD)296
Skewness2.0121562
Sum91644634
Variance425866.03
MonotonicityNot monotonic
2023-07-24T21:22:02.773025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 711
 
0.6%
1893 667
 
0.6%
348 648
 
0.5%
903 594
 
0.5%
492 594
 
0.5%
245 587
 
0.5%
366 537
 
0.5%
236 516
 
0.4%
340 487
 
0.4%
919 442
 
0.4%
Other values (2950) 110818
93.0%
(Missing) 2542
 
2.1%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 7
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 4
< 0.1%
28 2
 
< 0.1%
30 8
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 6
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing2542
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2.2051612
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:02.879049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7174519
Coefficient of variation (CV)0.7788328
Kurtosis4.8200793
Mean2.2051612
Median Absolute Deviation (MAD)0
Skewness1.9087497
Sum257124
Variance2.9496409
MonotonicityNot monotonic
2023-07-24T21:22:02.966069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 58957
49.5%
2 23054
 
19.3%
3 12978
 
10.9%
4 8863
 
7.4%
5 5599
 
4.7%
6 3945
 
3.3%
7 1560
 
1.3%
8 774
 
0.6%
10 354
 
0.3%
9 318
 
0.3%
Other values (9) 199
 
0.2%
(Missing) 2542
 
2.1%
ValueCountFrequency (%)
1 58957
49.5%
2 23054
 
19.3%
3 12978
 
10.9%
4 8863
 
7.4%
5 5599
 
4.7%
6 3945
 
3.3%
7 1560
 
1.3%
8 774
 
0.6%
9 318
 
0.3%
10 354
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 60
 
0.1%
11 73
 
0.1%
10 354
0.3%

product_weight_g
Real number (ℝ)

Distinct2204
Distinct (%)1.9%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean2112.2507
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:03.311660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3786.6951
Coefficient of variation (CV)1.7927299
Kurtosis16.01826
Mean2112.2507
Median Absolute Deviation (MAD)500
Skewness3.5830918
Sum2.4985814 × 108
Variance14339060
MonotonicityNot monotonic
2023-07-24T21:22:03.425946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 7093
 
6.0%
150 5410
 
4.5%
250 4741
 
4.0%
300 4429
 
3.7%
400 3787
 
3.2%
100 3666
 
3.1%
350 3291
 
2.8%
500 2856
 
2.4%
600 2838
 
2.4%
700 2148
 
1.8%
Other values (2194) 78031
65.5%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 991
0.8%
53 2
 
< 0.1%
54 2
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 303
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean30.265145
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:03.546903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.189367
Coefficient of variation (CV)0.53491788
Kurtosis3.6785662
Mean30.265145
Median Absolute Deviation (MAD)8
Skewness1.7456849
Sum3580064
Variance262.09561
MonotonicityNot monotonic
2023-07-24T21:22:03.655950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 18363
 
15.4%
20 10999
 
9.2%
30 7951
 
6.7%
17 6202
 
5.2%
18 5909
 
5.0%
19 4898
 
4.1%
25 4871
 
4.1%
40 4360
 
3.7%
22 4000
 
3.4%
50 3163
 
2.7%
Other values (89) 47574
39.9%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 96
 
0.1%
12 41
 
< 0.1%
13 60
 
0.1%
14 138
 
0.1%
15 220
 
0.2%
16 18363
15.4%
ValueCountFrequency (%)
105 335
0.3%
104 35
 
< 0.1%
103 46
 
< 0.1%
102 60
 
0.1%
101 108
 
0.1%
100 429
0.4%
99 36
 
< 0.1%
98 50
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean16.619706
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:03.769876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.453584
Coefficient of variation (CV)0.80949592
Kurtosis7.2778781
Mean16.619706
Median Absolute Deviation (MAD)6
Skewness2.2389625
Sum1965945
Variance180.99892
MonotonicityNot monotonic
2023-07-24T21:22:03.882923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10374
 
8.7%
20 6915
 
5.8%
15 6896
 
5.8%
12 6520
 
5.5%
11 6432
 
5.4%
2 5254
 
4.4%
4 4910
 
4.1%
8 4873
 
4.1%
5 4776
 
4.0%
16 4765
 
4.0%
Other values (92) 56575
47.5%
ValueCountFrequency (%)
2 5254
4.4%
3 2821
 
2.4%
4 4910
4.1%
5 4776
4.0%
6 3576
 
3.0%
7 4387
3.7%
8 4873
4.1%
9 3408
 
2.9%
10 10374
8.7%
11 6432
5.4%
ValueCountFrequency (%)
105 139
0.1%
104 14
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 43
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct95
Distinct (%)0.1%
Missing853
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean23.074799
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:03.996892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.749139
Coefficient of variation (CV)0.50917622
Kurtosis4.5530162
Mean23.074799
Median Absolute Deviation (MAD)6
Skewness1.707171
Sum2729518
Variance138.04227
MonotonicityNot monotonic
2023-07-24T21:22:04.103249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12701
 
10.7%
11 11144
 
9.4%
15 9376
 
7.9%
16 8810
 
7.4%
30 8045
 
6.8%
12 5711
 
4.8%
13 5491
 
4.6%
14 4846
 
4.1%
18 4192
 
3.5%
40 4157
 
3.5%
Other values (85) 43817
36.8%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 29
 
< 0.1%
9 51
 
< 0.1%
10 83
 
0.1%
11 11144
9.4%
12 5711
4.8%
13 5491
4.6%
14 4846
4.1%
15 9376
7.9%
ValueCountFrequency (%)
118 8
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 43
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%
Distinct96096
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:04.336120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3812576
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81601 ?
Unique (%)68.5%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd row7c396fd4830fd04220f754e42b4e5bff
4th row3a51803cc0d012c3b5dc8b7528cb05f7
5th rowef0996a1a279c26e7ecbd737be23d235
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c3 75
 
0.1%
6fbc7cdadbb522125f4b27ae9dee4060 38
 
< 0.1%
f9ae226291893fda10af7965268fb7f6 35
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a 29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e381 26
 
< 0.1%
85963fd37bfd387aa6d915d8a1065486 24
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c 24
 
< 0.1%
1d2435aa3b858d45c707c9fc25e18779 24
 
< 0.1%
5419a7c9b86a43d8140e2939cd2c2f7e 24
 
< 0.1%
c8460e4251689ba205045f3ea17884a1 24
 
< 0.1%
Other values (96086) 118820
99.7%
2023-07-24T21:22:04.663668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 239302
 
6.3%
b 238901
 
6.3%
1 238720
 
6.3%
a 238717
 
6.3%
d 238546
 
6.3%
3 238474
 
6.3%
8 238431
 
6.3%
e 238285
 
6.2%
5 238244
 
6.2%
2 238230
 
6.2%
Other values (6) 1426726
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2382587
62.5%
Lowercase Letter 1429989
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 239302
10.0%
1 238720
10.0%
3 238474
10.0%
8 238431
10.0%
5 238244
10.0%
2 238230
10.0%
9 238131
10.0%
7 238000
10.0%
0 237831
10.0%
4 237224
10.0%
Lowercase Letter
ValueCountFrequency (%)
b 238901
16.7%
a 238717
16.7%
d 238546
16.7%
e 238285
16.7%
f 238017
16.6%
c 237523
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2382587
62.5%
Latin 1429989
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 239302
10.0%
1 238720
10.0%
3 238474
10.0%
8 238431
10.0%
5 238244
10.0%
2 238230
10.0%
9 238131
10.0%
7 238000
10.0%
0 237831
10.0%
4 237224
10.0%
Latin
ValueCountFrequency (%)
b 238901
16.7%
a 238717
16.7%
d 238546
16.7%
e 238285
16.7%
f 238017
16.6%
c 237523
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3812576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 239302
 
6.3%
b 238901
 
6.3%
1 238720
 
6.3%
a 238717
 
6.3%
d 238546
 
6.3%
3 238474
 
6.3%
8 238431
 
6.3%
e 238285
 
6.2%
5 238244
 
6.2%
2 238230
 
6.2%
Other values (6) 1426726
37.4%

customer_zip_code_prefix
Real number (ℝ)

Distinct14994
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35033.451
Minimum1003
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:04.790080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3275
Q111250
median24240
Q358475
95-th percentile90570
Maximum99990
Range98987
Interquartile range (IQR)47225

Descriptive statistics

Standard deviation29823.199
Coefficient of variation (CV)0.85127779
Kurtosis-0.78102574
Mean35033.451
Median Absolute Deviation (MAD)16230
Skewness0.7854738
Sum4.1739905 × 109
Variance8.894232 × 108
MonotonicityNot monotonic
2023-07-24T21:22:04.903108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220 164
 
0.1%
22790 155
 
0.1%
22793 154
 
0.1%
24230 141
 
0.1%
22775 130
 
0.1%
35162 125
 
0.1%
29101 119
 
0.1%
11740 111
 
0.1%
13087 108
 
0.1%
38400 106
 
0.1%
Other values (14984) 117830
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 4
< 0.1%
1009 8
< 0.1%
1011 6
< 0.1%
1012 3
 
< 0.1%
1013 3
 
< 0.1%
ValueCountFrequency (%)
99990 1
 
< 0.1%
99980 3
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 2
 
< 0.1%
99955 3
 
< 0.1%
99950 9
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%
Distinct4119
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:05.110673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.33532
Min length3

Characters and Unicode

Total characters1231381
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1036 ?
Unique (%)0.9%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowsao paulo
4th rowsao paulo
5th rowsao paulo
ValueCountFrequency (%)
sao 25445
 
12.2%
paulo 18957
 
9.1%
de 11657
 
5.6%
rio 9967
 
4.8%
janeiro 8311
 
4.0%
do 5095
 
2.4%
belo 3373
 
1.6%
horizonte 3327
 
1.6%
brasilia 2510
 
1.2%
porto 1998
 
1.0%
Other values (3285) 118347
56.6%
2023-07-24T21:22:05.438088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1140982
92.7%
Space Separator 89844
 
7.3%
Dash Punctuation 290
 
< 0.1%
Other Punctuation 263
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 203082
17.8%
o 151991
13.3%
i 94150
 
8.3%
r 91236
 
8.0%
e 79953
 
7.0%
s 75446
 
6.6%
n 54566
 
4.8%
u 54064
 
4.7%
l 53676
 
4.7%
p 44782
 
3.9%
Other values (16) 238036
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
89844
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 290
100.0%
Other Punctuation
ValueCountFrequency (%)
' 263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140982
92.7%
Common 90399
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 203082
17.8%
o 151991
13.3%
i 94150
 
8.3%
r 91236
 
8.0%
e 79953
 
7.0%
s 75446
 
6.6%
n 54566
 
4.8%
u 54064
 
4.7%
l 53676
 
4.7%
p 44782
 
3.9%
Other values (16) 238036
20.9%
Common
ValueCountFrequency (%)
89844
99.4%
- 290
 
0.3%
' 263
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1231381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 203082
16.5%
o 151991
12.3%
i 94150
 
7.6%
r 91236
 
7.4%
89844
 
7.3%
e 79953
 
6.5%
s 75446
 
6.1%
n 54566
 
4.4%
u 54064
 
4.4%
l 53676
 
4.4%
Other values (21) 283373
23.0%

customer_state
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SP
50265 
RJ
15518 
MG
13819 
RS
6573 
PR
6043 
Other values (22)
26925 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters238286
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 50265
42.2%
RJ 15518
 
13.0%
MG 13819
 
11.6%
RS 6573
 
5.5%
PR 6043
 
5.1%
SC 4345
 
3.6%
BA 4091
 
3.4%
DF 2516
 
2.1%
GO 2466
 
2.1%
ES 2360
 
2.0%
Other values (17) 11147
 
9.4%

Length

2023-07-24T21:22:05.548113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 50265
42.2%
rj 15518
 
13.0%
mg 13819
 
11.6%
rs 6573
 
5.5%
pr 6043
 
5.1%
sc 4345
 
3.6%
ba 4091
 
3.4%
df 2516
 
2.1%
go 2466
 
2.1%
es 2360
 
2.0%
Other values (17) 11147
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 238286
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 238286
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 64808
27.2%
P 60647
25.5%
R 29104
12.2%
M 16842
 
7.1%
G 16285
 
6.8%
J 15518
 
6.5%
A 6892
 
2.9%
E 6234
 
2.6%
C 6005
 
2.5%
B 4735
 
2.0%
Other values (7) 11216
 
4.7%

seller_zip_code_prefix
Real number (ℝ)

Distinct2246
Distinct (%)1.9%
Missing833
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean24442.41
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-24T21:22:05.648640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2972
Q16429
median13660
Q327972
95-th percentile88308
Maximum99730
Range98729
Interquartile range (IQR)21543

Descriptive statistics

Standard deviation27573.005
Coefficient of variation (CV)1.1280804
Kurtosis0.93697995
Mean24442.41
Median Absolute Deviation (MAD)8123
Skewness1.5561361
Sum2.8917816 × 109
Variance7.6027058 × 108
MonotonicityNot monotonic
2023-07-24T21:22:05.764688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14940 8373
 
7.0%
5849 2145
 
1.8%
15025 2098
 
1.8%
9015 1899
 
1.6%
13405 1678
 
1.4%
8577 1556
 
1.3%
4782 1549
 
1.3%
3204 1477
 
1.2%
4160 1268
 
1.1%
13232 1255
 
1.1%
Other values (2236) 95012
79.7%
ValueCountFrequency (%)
1001 22
 
< 0.1%
1021 41
 
< 0.1%
1022 5
 
< 0.1%
1023 5
 
< 0.1%
1026 323
0.3%
1031 129
 
0.1%
1035 18
 
< 0.1%
1039 1
 
< 0.1%
1040 25
 
< 0.1%
1041 2
 
< 0.1%
ValueCountFrequency (%)
99730 12
 
< 0.1%
99700 2
 
< 0.1%
99670 1
 
< 0.1%
99500 61
0.1%
99300 2
 
< 0.1%
98975 22
 
< 0.1%
98920 2
 
< 0.1%
98910 14
 
< 0.1%
98803 66
0.1%
98780 4
 
< 0.1%
Distinct611
Distinct (%)0.5%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
2023-07-24T21:22:05.953129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length31
Mean length10.102451
Min length2

Characters and Unicode

Total characters1195221
Distinct characters41
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowmaua
2nd rowmaua
3rd rowmaua
4th rowmaua
5th rowmaua
ValueCountFrequency (%)
sao 36362
 
17.9%
paulo 29574
 
14.5%
ibitinga 8373
 
4.1%
rio 5930
 
2.9%
do 5524
 
2.7%
preto 5518
 
2.7%
de 4192
 
2.1%
jose 4085
 
2.0%
santo 3270
 
1.6%
andre 3164
 
1.6%
Other values (640) 97267
47.9%
2023-07-24T21:22:06.276728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (31) 266265
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1108993
92.8%
Space Separator 85009
 
7.1%
Other Punctuation 614
 
0.1%
Modifier Symbol 369
 
< 0.1%
Dash Punctuation 164
 
< 0.1%
Close Punctuation 31
 
< 0.1%
Open Punctuation 31
 
< 0.1%
Decimal Number 8
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 198772
17.9%
o 146215
13.2%
i 102111
9.2%
r 78220
 
7.1%
s 76216
 
6.9%
e 64170
 
5.8%
u 62907
 
5.7%
p 58419
 
5.3%
l 56917
 
5.1%
t 47317
 
4.3%
Other values (14) 217729
19.6%
Other Punctuation
ValueCountFrequency (%)
' 347
56.5%
/ 141
23.0%
. 76
 
12.4%
@ 38
 
6.2%
\ 6
 
1.0%
, 6
 
1.0%
Decimal Number
ValueCountFrequency (%)
4 2
25.0%
2 2
25.0%
5 2
25.0%
0 1
12.5%
8 1
12.5%
Space Separator
ValueCountFrequency (%)
85009
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 369
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1108993
92.8%
Common 86226
 
7.2%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 198772
17.9%
o 146215
13.2%
i 102111
9.2%
r 78220
 
7.1%
s 76216
 
6.9%
e 64170
 
5.8%
u 62907
 
5.7%
p 58419
 
5.3%
l 56917
 
5.1%
t 47317
 
4.3%
Other values (14) 217729
19.6%
Common
ValueCountFrequency (%)
85009
98.6%
´ 369
 
0.4%
' 347
 
0.4%
- 164
 
0.2%
/ 141
 
0.2%
. 76
 
0.1%
@ 38
 
< 0.1%
) 31
 
< 0.1%
( 31
 
< 0.1%
\ 6
 
< 0.1%
Other values (6) 14
 
< 0.1%
Inherited
ValueCountFrequency (%)
̃ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194850
> 99.9%
None 369
 
< 0.1%
Diacriticals 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 198772
16.6%
o 146215
12.2%
i 102111
 
8.5%
85009
 
7.1%
r 78220
 
6.5%
s 76216
 
6.4%
e 64170
 
5.4%
u 62907
 
5.3%
p 58419
 
4.9%
l 56917
 
4.8%
Other values (29) 265894
22.3%
None
ValueCountFrequency (%)
´ 369
100.0%
Diacriticals
ValueCountFrequency (%)
̃ 2
100.0%

seller_state
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct23
Distinct (%)< 0.1%
Missing833
Missing (%)0.7%
Memory size1.8 MiB
SP
84377 
MG
9314 
PR
9096 
RJ
 
5036
SC
 
4271
Other values (18)
 
6216

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236620
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 84377
70.8%
MG 9314
 
7.8%
PR 9096
 
7.6%
RJ 5036
 
4.2%
SC 4271
 
3.6%
RS 2294
 
1.9%
DF 949
 
0.8%
BA 700
 
0.6%
GO 550
 
0.5%
PE 465
 
0.4%
Other values (13) 1258
 
1.1%
(Missing) 833
 
0.7%

Length

2023-07-24T21:22:06.385696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 84377
71.3%
mg 9314
 
7.9%
pr 9096
 
7.7%
rj 5036
 
4.3%
sc 4271
 
3.6%
rs 2294
 
1.9%
df 949
 
0.8%
ba 700
 
0.6%
go 550
 
0.5%
pe 465
 
0.4%
Other values (13) 1258
 
1.1%

Most occurring characters

ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 236620
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 236620
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 94002
39.7%
S 91402
38.6%
R 16496
 
7.0%
M 9934
 
4.2%
G 9864
 
4.2%
J 5036
 
2.1%
C 4375
 
1.8%
A 1122
 
0.5%
E 968
 
0.4%
D 949
 
0.4%
Other values (6) 2472
 
1.0%

Interactions

2023-07-24T21:21:48.460775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:25.648653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.293037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.924404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.416042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.182439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.706310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.307565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.899923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.799277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.375697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.999089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.496919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.301852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.886915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.581801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:25.769681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.403062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.031429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.526066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.291464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.819338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.419590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:37.012948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.908304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.489735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.107112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.846001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.415878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.006943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.694827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:25.886707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.510085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.132451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.633090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.398488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.923364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.527614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:37.371029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.016833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.601774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.208135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.955025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.524902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.119968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.788849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:25.989731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.606107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.220470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.727111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.490012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.025388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.623637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:37.472052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.114858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.702796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.302663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.059048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.619924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.215990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.891871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.097756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.714131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.322493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.828134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.590035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.134412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.723658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:37.581076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.219887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.823824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.400687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.162071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.722947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.319014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.990894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.203779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.816153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.414514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.134203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.684059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.235318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.821680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:37.689101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.320915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.931847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.502712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.257093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.830971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.418035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.098919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.339854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.923178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.517537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.239226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.788085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.340343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.924704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:37.830133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.427942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.039872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.603873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.369118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.950999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.524059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.203942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.445846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.033205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.613559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.360253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.890109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.450367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.025727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:37.936157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.532970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.145897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.705742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.471648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.057022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.626082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.311967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.556870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.171234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.720583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.468279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.999136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.560392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.136751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.048187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.644998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.258923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.812768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.582671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.165046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.746109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.417990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.666895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.305264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.820606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.581305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.105162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.673417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.250777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.158212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.756025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.368946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:42.919794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.689695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.276071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.859135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.528016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.780921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.426292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:29.931632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.692329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.215189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.787443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.391809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.274240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.870053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.478971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.022823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.801720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.385096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:47.969664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.642041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.880943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.523314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.031653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.786349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.311211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.887466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.502834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.374264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:39.970076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.578994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.113844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.899742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.489675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.064685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.748644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:26.980966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.619336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.125976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.884372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.411236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:34.991491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.601856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.478290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.070101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.684017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.206853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:44.999765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.584755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.167708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.847666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.083990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.720358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.220997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:31.981394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.507260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.098513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.700878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.584224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.173126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.787040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.300874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.096787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.681871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.264730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:49.947688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:27.190014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:28.821381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:30.319020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:32.082416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:33.607285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:35.203541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:36.798902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:38.692251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:40.274661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:41.896065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:43.397897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:45.202812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:46.783893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-24T21:21:48.362752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-24T21:22:06.480724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
order_item_idpricefreight_valuepayment_sequentialpayment_installmentspayment_valueproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmcustomer_zip_code_prefixseller_zip_code_prefixorder_statuspayment_typereview_scorecustomer_stateseller_state
order_item_id1.000-0.116-0.056-0.0080.0610.257-0.021-0.032-0.0650.0000.0070.018-0.004-0.009-0.0110.0020.0210.0410.0130.000
price-0.1161.0000.433-0.0050.3150.7890.0420.2110.0290.5140.2660.3270.2710.0700.1750.0140.0140.0120.0200.052
freight_value-0.0560.4331.0000.0170.1910.4230.0340.1170.0110.4480.2840.2840.2750.4660.2570.0150.0090.0150.0850.048
payment_sequential-0.008-0.0050.0171.000-0.178-0.215-0.003-0.013-0.0050.0300.0340.0130.028-0.0090.0070.0260.1980.0120.0260.016
payment_installments0.0610.3150.191-0.1781.0000.3950.0160.033-0.0030.1980.1090.1060.1250.0690.0650.0050.2360.0270.0320.033
payment_value0.2570.7890.423-0.2150.3951.0000.0250.169-0.0110.4490.2290.3050.2320.1060.1600.0150.0180.0280.0290.036
product_name_lenght-0.0210.0420.034-0.0030.0160.0251.0000.0730.1630.0760.060-0.0570.0650.0150.0090.0190.0110.0130.0130.070
product_description_lenght-0.0320.2110.117-0.0130.0330.1690.0731.0000.1110.095-0.0210.135-0.0810.0310.0010.0160.0200.0150.0260.112
product_photos_qty-0.0650.0290.011-0.005-0.003-0.0110.1630.1111.0000.0030.005-0.079-0.0150.026-0.0780.0130.0040.0160.0140.040
product_weight_g0.0000.5140.4480.0300.1980.4490.0760.0950.0031.0000.6190.5320.6210.0260.0960.0110.0180.0200.0280.078
product_length_cm0.0070.2660.2840.0340.1090.2290.060-0.0210.0050.6191.0000.2480.6320.0080.0670.0140.0220.0180.0170.084
product_height_cm0.0180.3270.2840.0130.1060.305-0.0570.135-0.0790.5320.2481.0000.3380.0190.0490.0160.0150.0180.0190.065
product_width_cm-0.0040.2710.2750.0280.1250.2320.065-0.081-0.0150.6210.6320.3381.000-0.0020.0770.0040.0200.0140.0160.057
customer_zip_code_prefix-0.0090.0700.466-0.0090.0690.1060.0150.0310.0260.0260.0080.019-0.0021.0000.0600.0220.0290.0410.8960.065
seller_zip_code_prefix-0.0110.1750.2570.0070.0650.1600.0090.001-0.0780.0960.0670.0490.0770.0601.0000.0130.0180.0210.0690.920
order_status0.0020.0140.0150.0260.0050.0150.0190.0160.0130.0110.0140.0160.0040.0220.0131.0000.0370.1490.0260.029
payment_type0.0210.0140.0090.1980.2360.0180.0110.0200.0040.0180.0220.0150.0200.0290.0180.0371.0000.0100.0330.021
review_score0.0410.0120.0150.0120.0270.0280.0130.0150.0160.0200.0180.0180.0140.0410.0210.1490.0101.0000.0480.023
customer_state0.0130.0200.0850.0260.0320.0290.0130.0260.0140.0280.0170.0190.0160.8960.0690.0260.0330.0481.0000.053
seller_state0.0000.0520.0480.0160.0330.0360.0700.1120.0400.0780.0840.0650.0570.0650.9200.0290.0210.0230.0531.000

Missing values

2023-07-24T21:21:50.681863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-24T21:21:51.601723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-24T21:21:53.237948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

order_idcustomer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_dateorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valuepayment_sequentialpayment_typepayment_installmentspayment_valuereview_idreview_scorereview_comment_titlereview_comment_messagereview_creation_datereview_answer_timestampproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmcustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateseller_zip_code_prefixseller_cityseller_state
0e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:001.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.721.0credit_card1.018.12a54f0611adc9ed256b57ede6b6eb51144.0NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:48utilidades_domesticas40.0268.04.0500.019.08.013.07c396fd4830fd04220f754e42b4e5bff3149sao pauloSP9350.0mauaSP
1e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:001.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.723.0voucher1.02.00a54f0611adc9ed256b57ede6b6eb51144.0NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:48utilidades_domesticas40.0268.04.0500.019.08.013.07c396fd4830fd04220f754e42b4e5bff3149sao pauloSP9350.0mauaSP
2e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:001.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.722.0voucher1.018.59a54f0611adc9ed256b57ede6b6eb51144.0NaNNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.2017-10-11 00:00:002017-10-12 03:43:48utilidades_domesticas40.0268.04.0500.019.08.013.07c396fd4830fd04220f754e42b4e5bff3149sao pauloSP9350.0mauaSP
3128e10d95713541c87cd1a2e48201934a20e8105f23924cd00833fd87daa0831delivered2017-08-15 18:29:312017-08-15 20:05:162017-08-17 15:28:332017-08-18 14:44:432017-08-28 00:00:001.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-21 20:05:1629.997.781.0credit_card3.037.77b46f1e34512b0f4c74a72398b03ca7884.0NaNDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.2017-08-19 00:00:002017-08-20 15:16:36utilidades_domesticas40.0268.04.0500.019.08.013.03a51803cc0d012c3b5dc8b7528cb05f73366sao pauloSP9350.0mauaSP
40e7e841ddf8f8f2de2bad69267ecfbcf26c7ac168e1433912a51b924fbd34d34delivered2017-08-02 18:24:472017-08-02 18:43:152017-08-04 17:35:432017-08-07 18:30:012017-08-15 00:00:001.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-08 18:37:3129.997.781.0credit_card1.037.77dc90f19c2806f1abba9e72ad3c3500735.0NaNSó achei ela pequena pra seis xícaras ,mais é um bom produto\r\n2017-08-08 00:00:002017-08-08 23:26:23utilidades_domesticas40.0268.04.0500.019.08.013.0ef0996a1a279c26e7ecbd737be23d2352290sao pauloSP9350.0mauaSP
5bfc39df4f36c3693ff3b63fcbea9e90a53904ddbea91e1e92b2b3f1d09a7af86delivered2017-10-23 23:26:462017-10-25 02:14:112017-10-27 16:48:462017-11-07 18:04:592017-11-13 00:00:001.087285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-31 02:14:1129.9914.101.0boleto1.044.091bafb430e498b939f258b9c9dbdff9b13.0NaNNaN2017-11-08 00:00:002017-11-10 19:52:38utilidades_domesticas40.0268.04.0500.019.08.013.0e781fdcc107d13d865fc7698711cc57288032florianopolisSC9350.0mauaSP
68736140c61ea584cb4250074756d8f3bab8844663ae049fda8baf15fc928f47fdelivered2017-08-10 13:35:552017-08-10 13:50:092017-08-11 13:52:352017-08-16 19:03:362017-08-23 00:00:001.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d92017-08-16 13:50:0975.907.791.0credit_card1.083.69b8238c6515192f8129081e17dc57d1695.0NaNcusto beneficio, simples de usar e rápido2017-08-17 00:00:002017-08-21 12:43:27bebes58.0398.03.0238.020.010.015.002c9e0c05a817d4562ec0e8c90f29dba8577itaquaquecetubaSP9350.0mauaSP
788407c8c6e12493ff6e845df39540112e902cb9d9992a69a267f69dec57aa3a3delivered2017-08-15 02:03:012017-08-15 02:15:132017-08-16 15:52:292017-08-25 21:59:262017-08-28 00:00:001.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d92017-08-21 02:15:1375.907.791.0credit_card2.083.69186b702b3817fd5cc00b201b11764d634.0NaNmuitro bom o produto chegou dentro do prazo.2017-08-26 00:00:002017-08-28 20:10:38bebes58.0398.03.0238.020.010.015.028adbfbaf0b9c5e5a0555a8c853a753413060campinasSP9350.0mauaSP
84f2acff0b7d2bcc4a408abe5a223d407d67b6cca5a87299f711a6961f579fe67delivered2017-08-01 16:31:352017-08-02 02:50:252017-08-03 14:36:342017-08-09 19:56:502017-08-23 00:00:001.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d92017-08-08 02:50:2575.9014.281.0boleto1.090.18567900cb1263f2ee7341989937a789cc5.0NaNNaN2017-08-10 00:00:002017-08-11 21:08:38bebes58.0398.03.0238.020.010.015.0aea90564d6f09ae11bf936f55ed49d7282030curitibaPR9350.0mauaSP
9019aaee09698daf81dcffe9d94a18b5ce3893e579755de4feb1a4d0313c103fadelivered2017-08-10 14:04:582017-08-10 14:23:382017-08-11 13:52:352017-08-12 11:56:492017-08-23 00:00:001.0b00a32a0b42fd65efb58a5822009f6293504c0cb71d7fa48d967e0e4c94d59d92017-08-16 14:23:3875.907.791.0credit_card2.083.6943334848a48a7abf6faa2f8aba675b8a2.0NaNtudo correu bem com a loja e com a entrega mas o produto não funcionou, vou devolver2017-08-13 00:00:002017-08-14 12:24:58bebes58.0398.03.0238.020.010.015.0cd6b577df45c00daa6b2767eaa947c7213092campinasSP9350.0mauaSP
order_idcustomer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_dateorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valuepayment_sequentialpayment_typepayment_installmentspayment_valuereview_idreview_scorereview_comment_titlereview_comment_messagereview_creation_datereview_answer_timestampproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmcustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateseller_zip_code_prefixseller_cityseller_state
119133f5f8998eee8ec7bc513dc52847d64ce0f4656b824844a039a87fd9c51ad3586acanceled2018-03-01 11:42:232018-03-01 12:20:32NaNNaN2018-03-20 00:00:001.051bd37bb8517d5bfdb1f54c11fb01d27f09e26011d833ddab11593c1a097a92a2018-03-08 12:20:3279.9022.191.0credit_card2.0102.09bdf24af3e04cf534d9bee6afd037c1a01.0NaNNaN2018-03-22 00:00:002018-03-26 03:00:14moveis_decoracao43.087.01.03500.020.020.020.0149164aee69ed656dedbbe68623157bc13469americanaSP13632.0pirassunungaSP
1191345bacbd9f42bd029c3a296501224e193e5a1470d43d8ad960d4199134d3df48e0delivered2018-08-10 21:14:352018-08-10 21:25:222018-08-13 13:54:002018-08-21 04:16:312018-08-30 00:00:001.0710e8b076db06c8e5343a9e23f0e3d838dd386be0767c330276ea6a3f96532d32018-08-15 21:25:2244.9922.251.0credit_card2.0134.48f91f12b20162095a387a237e114e7d675.0NaNNaN2018-08-21 00:00:002018-08-21 22:01:55esporte_lazer60.0645.02.0600.030.020.020.00b39f417a3c099ff0497346258e8d75239810caraiMG88490.0paulo lopesSC
1191355bacbd9f42bd029c3a296501224e193e5a1470d43d8ad960d4199134d3df48e0delivered2018-08-10 21:14:352018-08-10 21:25:222018-08-13 13:54:002018-08-21 04:16:312018-08-30 00:00:002.0710e8b076db06c8e5343a9e23f0e3d838dd386be0767c330276ea6a3f96532d32018-08-15 21:25:2244.9922.251.0credit_card2.0134.48f91f12b20162095a387a237e114e7d675.0NaNNaN2018-08-21 00:00:002018-08-21 22:01:55esporte_lazer60.0645.02.0600.030.020.020.00b39f417a3c099ff0497346258e8d75239810caraiMG88490.0paulo lopesSC
1191365a8a4dc28b16fb90469ad749f9535773c0c8b8bb055100a0cc08dcc04d847ac9canceled2018-03-13 10:58:092018-03-14 03:08:35NaNNaN2018-03-23 00:00:001.033ac889bc3af4ddede9c14fc789a3743666658b8da8370f30e1f89893b1de5e62018-03-20 03:08:35149.0011.671.0boleto1.0321.34ec03cc18869f8509f9d3fbe2d106cea75.0NaNNaN2018-03-28 00:00:002018-03-28 18:11:45ferramentas_jardim28.0682.01.01700.030.05.030.082ec5f749b66f1857e868b6414a67ab36765taboao da serraSP3658.0sao pauloSP
1191375a8a4dc28b16fb90469ad749f9535773c0c8b8bb055100a0cc08dcc04d847ac9canceled2018-03-13 10:58:092018-03-14 03:08:35NaNNaN2018-03-23 00:00:002.033ac889bc3af4ddede9c14fc789a3743666658b8da8370f30e1f89893b1de5e62018-03-20 03:08:35149.0011.671.0boleto1.0321.34ec03cc18869f8509f9d3fbe2d106cea75.0NaNNaN2018-03-28 00:00:002018-03-28 18:11:45ferramentas_jardim28.0682.01.01700.030.05.030.082ec5f749b66f1857e868b6414a67ab36765taboao da serraSP3658.0sao pauloSP
1191381ab38815794efa43d269d62b98dae815a0b67404d84a70ef420a7f99ad6b190adelivered2018-07-01 10:23:102018-07-05 16:17:522018-07-04 14:34:002018-07-09 15:06:572018-07-20 00:00:001.031ec3a565e06de4bdf9d2a511b822b4dbabcc0ab201e4c60188427cae51a5b8b2018-07-10 08:32:3379.0014.131.0boleto1.093.137f9849fcbfdf9fa3070c05b5501bf0665.0NaNNaN2018-07-10 00:00:002018-07-10 18:32:29construcao_ferramentas_iluminacao40.0516.02.0750.030.028.028.02077f7ec37df79c62cc24b7b8f30e8c98528ferraz de vasconcelosSP13660.0porto ferreiraSP
119139b159d0ce7cd881052da94fa165617b05e0c3bc5ce0836b975d6b2a8ce7bb0e3ecanceled2017-03-11 19:51:362017-03-11 19:51:36NaNNaN2017-03-30 00:00:001.0241a1ffc9cf969b27de6e723010202688501d82f68d23148b6d78bb7c4a420372017-03-16 19:51:3619.7010.961.0credit_card1.030.66c950324a42c5796d06f569f77d8b2e881.0NaNNaN2017-04-01 00:00:002017-04-01 10:24:03automotivo48.0260.02.0400.016.04.011.078a159045124eb7601951b917a42034f89111gasparSC89031.0blumenauSC
119140735dce2d574afe8eb87e80a3d6229c48d531d01affc2c55769f6b9ed410d8d3cdelivered2018-07-24 09:46:272018-07-24 11:24:272018-07-24 15:14:002018-08-02 22:47:352018-08-16 00:00:001.01d187e8e7a30417fda31e85679d96f0fd263fa444c1504a75cbca5cc465f592a2018-07-30 11:24:27399.0045.071.0debit_card1.0444.0719f21ead7ffe5b1b5147a7877c22bae55.0NaNNaN2018-08-03 00:00:002018-08-04 11:22:40moveis_decoracao43.0729.02.02100.080.08.030.08cf3c6e1d2c8afaab2eda3fa01d4e3d260455fortalezaCE13478.0americanaSP
11914125d2bfa43663a23586afd12f15b542e79d8c06734fde9823ace11a4b5929b5a7delivered2018-05-22 21:13:212018-05-22 21:35:402018-05-24 12:28:002018-06-12 23:11:292018-06-08 00:00:001.06e1c2008dea1929b9b6c27fa01381e90edf3fabebcc20f7463cc9c53da932ea82018-05-28 21:31:24219.9024.121.0credit_card4.0244.02ec2817e750153dfdd61894780dfc5d9e4.0NaNNaN2018-06-10 00:00:002018-06-13 09:17:47moveis_decoracao19.0531.01.05900.041.021.041.0e55e436481078787e32349cee9febf5e39803teofilo otoniMG8320.0sao pauloSP
1191421565f22aa9452ff278638e87cc89567856772dfbcbe7df908a284ff0d53adf7ddelivered2018-05-15 17:41:002018-05-16 03:35:292018-05-16 17:20:002018-05-21 14:31:412018-05-29 00:00:001.09c1e194db1d35a79d962ea610bfe0868f3862c2188522d89860c38a3ea8b550d2018-05-22 03:35:2915.5012.791.0boleto1.028.29cbb879403973e209b4df371a5dafbaa75.0NaNNaN2018-06-01 00:00:002018-06-01 15:14:23perfumaria40.0871.01.083.017.08.013.06ceea7c1088e15ab3c67980a2d9bb3099687sao bernardo do campoSP14092.0ribeirao pretoSP